Implementing Machine Unlearning for NIST AI 100-2e Compliance
By utilizing gradient-based unlearning (e.g., SISA or Gradient Ascent) to explicitly modify model parameter-sets rather than relying on output suppression, firms can achieve (epsilon, delta)-differential privacy, though they must balance the 'onion effect' where unlearning one point risks compromising the security of the retain-set.